Overview

Dataset statistics

Number of variables26
Number of observations998
Missing cells230
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory396.7 KiB
Average record size in memory407.0 B

Variable types

Categorical8
Numeric16
DateTime1
Boolean1

Dataset

DescriptionQuality-verified clinical data for JHB_DPHRU_053
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Variable descriptions

study_sourceStudy identifier
Age (at enrolment)Patient age at study enrollment
SexBiological sex
RaceRacial/ethnic group
enrollment_dateDate of study enrollment
visit_dateDate of clinic visit
primary_datePrimary reference date
study_armStudy treatment arm
study_visitStudy visit number
Antiretroviral Therapy StatusCurrent ART status
BMI (kg/m²)Body Mass Index
weight_kgBody weight in kilograms
height_mHeight in meters
Waist circumference (cm)Waist circumference in centimeters
hip_circumference_cmHip circumference in centimeters
waist_hip_ratioWaist-to-hip ratio
systolic_bp_mmHgSystolic blood pressure
diastolic_bp_mmHgDiastolic blood pressure
heart_rate_bpmHeart rate in beats per minute
Respiratory rate (breaths/min)Respiratory rate
Oxygen saturation (%)Oxygen saturation
body_temperature_celsiusBody temperature in Celsius
CD4 cell count (cells/µL)CD4+ T lymphocyte count
HIV viral load (copies/mL)HIV RNA copies per mL
cd4_percentCD4+ percentage
cd8_count_cells_uLCD8+ T lymphocyte count
cd4_cd8_ratioCD4/CD8 ratio
Hematocrit (%)Hematocrit
hemoglobin_g_dLHemoglobin concentration
White blood cell count (×10³/µL)Total WBC count
Red blood cell count (×10⁶/µL)Total RBC count
Platelet count (×10³/µL)Platelet count
MCV (MEAN CELL VOLUME)Mean corpuscular volume
mch_pgMean corpuscular hemoglobin
mchc_g_dLMean corpuscular hemoglobin concentration
RDWRed cell distribution width
Lymphocyte count (×10⁹/L)Lymphocyte absolute count
Neutrophil count (×10⁹/L)Neutrophil absolute count
Monocyte count (×10⁹/L)Monocyte absolute count
Eosinophil count (×10⁹/L)Eosinophil absolute count
Basophil count (×10⁹/L)Basophil absolute count
lymphocyte_percentLymphocyte percentage
neutrophil_percentNeutrophil percentage
monocyte_percentMonocyte percentage
eosinophil_percentEosinophil percentage
basophil_percentBasophil percentage
ALT (U/L)Alanine aminotransferase
AST (U/L)Aspartate aminotransferase
Alkaline phosphatase (U/L)Alkaline phosphatase
Total bilirubin (mg/dL)Total bilirubin
direct_bilirubin_mg_dLDirect bilirubin
indirect_bilirubin_mg_dLIndirect bilirubin
Albumin (g/dL)Serum albumin
Total protein (g/dL)Total serum protein
ggt_u_LGamma-glutamyl transferase
creatinine_umol_LSerum creatinine (µmol/L)
creatinine_mg_dLSerum creatinine (mg/dL)
creatinine clearanceEstimated creatinine clearance
bun_mg_dLBlood urea nitrogen
urea_mmol_LSerum urea
egfr_ml_minEstimated glomerular filtration rate
Sodium (mEq/L)Serum sodium
Potassium (mEq/L)Serum potassium
chloride_mEq_LSerum chloride
bicarbonate_mEq_LSerum bicarbonate
calcium_mg_dLSerum calcium
magnesium_mg_dLSerum magnesium
phosphate_mg_dLSerum phosphate
total_cholesterol_mg_dLTotal cholesterol
hdl_cholesterol_mg_dLHDL cholesterol
ldl_cholesterol_mg_dLLDL cholesterol
Triglycerides (mg/dL)Triglycerides
vldl_cholesterol_mg_dLVLDL cholesterol
cholesterol_hdl_ratioTotal cholesterol/HDL ratio
fasting_glucose_mmol_LFasting blood glucose (mmol/L)
glucose_mg_dLBlood glucose (mg/dL)
hba1c_percentGlycated hemoglobin
insulin_uIU_mLSerum insulin
lactate_mmol_LBlood lactate
crp_mg_LC-reactive protein
esr_mm_hrErythrocyte sedimentation rate
pt_secondsProthrombin time
inrInternational normalized ratio
aptt_secondsActivated partial thromboplastin time
uric_acid_mg_dLSerum uric acid
ldh_u_LLactate dehydrogenase
ck_u_LCreatine kinase
amylase_u_LSerum amylase
lipase_u_LSerum lipase
climate_daily_mean_tempDaily mean temperature
climate_daily_max_tempDaily maximum temperature
climate_daily_min_tempDaily minimum temperature
climate_temp_anomalyTemperature anomaly from baseline
climate_heat_day_p90Heat day indicator (>90th percentile)
climate_heat_day_p95Heat day indicator (>95th percentile)
climate_heat_stress_indexHeat stress index
climate_humidityRelative humidity
climate_precipitationPrecipitation
climate_seasonSeason
cd4_correction_appliedQuality flag: CD4 corrections applied
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circumference unit corrected
sa_biomarker_standardsSouth African biomarker reference standards applied

Alerts

study_source has constant value "JHB_DPHRU_053"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
waist_circ_unit_correction_applied has constant value "False"Constant
sa_biomarker_standards has constant value "1.0"Constant
climate_heat_day_p90 has constant value "0.0"Constant
climate_heat_day_p95 has constant value "0.0"Constant
BMI (kg/m²) is highly overall correlated with Sex and 1 other fieldsHigh correlation
Sex is highly overall correlated with BMI (kg/m²) and 1 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with climate_daily_mean_temp and 3 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_daily_max_temp and 2 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_daily_max_temp and 2 other fieldsHigh correlation
climate_season is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_seasonHigh correlation
diastolic_bp_mmHg is highly overall correlated with systolic_bp_mmHgHigh correlation
height_m is highly overall correlated with SexHigh correlation
systolic_bp_mmHg is highly overall correlated with diastolic_bp_mmHgHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²)High correlation
Albumin (g/dL) has 56 (5.6%) missing valuesMissing
Total protein (g/dL) has 69 (6.9%) missing valuesMissing
total_cholesterol_mg_dL has 26 (2.6%) missing valuesMissing
Triglycerides (mg/dL) has 26 (2.6%) missing valuesMissing
fasting_glucose_mmol_L has 24 (2.4%) missing valuesMissing

Reproduction

Analysis started2025-11-25 05:10:23.876258
Analysis finished2025-11-25 05:10:33.609550
Duration9.73 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Study identifier

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.2 KiB
JHB_DPHRU_053
998 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters12974
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_DPHRU_053
2nd rowJHB_DPHRU_053
3rd rowJHB_DPHRU_053
4th rowJHB_DPHRU_053
5th rowJHB_DPHRU_053

Common Values

ValueCountFrequency (%)
JHB_DPHRU_053998
100.0%

Length

2025-11-25T07:10:33.638293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:33.670363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_dphru_053998
100.0%

Most occurring characters

ValueCountFrequency (%)
H1996
15.4%
_1996
15.4%
J998
7.7%
B998
7.7%
D998
7.7%
P998
7.7%
R998
7.7%
U998
7.7%
0998
7.7%
5998
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7984
61.5%
Decimal Number2994
 
23.1%
Connector Punctuation1996
 
15.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H1996
25.0%
J998
12.5%
B998
12.5%
D998
12.5%
P998
12.5%
R998
12.5%
U998
12.5%
Decimal Number
ValueCountFrequency (%)
0998
33.3%
5998
33.3%
3998
33.3%
Connector Punctuation
ValueCountFrequency (%)
_1996
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7984
61.5%
Common4990
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
H1996
25.0%
J998
12.5%
B998
12.5%
D998
12.5%
P998
12.5%
R998
12.5%
U998
12.5%
Common
ValueCountFrequency (%)
_1996
40.0%
0998
20.0%
5998
20.0%
3998
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H1996
15.4%
_1996
15.4%
J998
7.7%
B998
7.7%
D998
7.7%
P998
7.7%
R998
7.7%
U998
7.7%
0998
7.7%
5998
7.7%

Age (at enrolment)
Real number (ℝ)

Patient age at study enrollment

Distinct29
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.599198
Minimum41
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:33.700332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile44
Q149
median53
Q359
95-th percentile63
Maximum71
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9694709
Coefficient of variation (CV)0.11137239
Kurtosis-0.97067221
Mean53.599198
Median Absolute Deviation (MAD)5
Skewness0.064769631
Sum53492
Variance35.634583
MonotonicityNot monotonic
2025-11-25T07:10:33.742681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5363
 
6.3%
5561
 
6.1%
4857
 
5.7%
6256
 
5.6%
4955
 
5.5%
5052
 
5.2%
5252
 
5.2%
4750
 
5.0%
5446
 
4.6%
6046
 
4.6%
Other values (19)460
46.1%
ValueCountFrequency (%)
414
 
0.4%
424
 
0.4%
4318
 
1.8%
4430
3.0%
4539
3.9%
4639
3.9%
4750
5.0%
4857
5.7%
4955
5.5%
5052
5.2%
ValueCountFrequency (%)
711
 
0.1%
681
 
0.1%
671
 
0.1%
663
 
0.3%
658
 
0.8%
6416
 
1.6%
6336
3.6%
6256
5.6%
6140
4.0%
6046
4.6%

Sex
Categorical

High correlation 

Biological sex

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
Male
501 
Female
497 

Length

Max length6
Median length4
Mean length4.995992
Min length4

Characters and Unicode

Total characters4986
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male501
50.2%
Female497
49.8%

Length

2025-11-25T07:10:33.789988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:33.825391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male501
50.2%
female497
49.8%

Most occurring characters

ValueCountFrequency (%)
e1495
30.0%
a998
20.0%
l998
20.0%
M501
 
10.0%
F497
 
10.0%
m497
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3988
80.0%
Uppercase Letter998
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1495
37.5%
a998
25.0%
l998
25.0%
m497
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
M501
50.2%
F497
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin4986
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1495
30.0%
a998
20.0%
l998
20.0%
M501
 
10.0%
F497
 
10.0%
m497
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1495
30.0%
a998
20.0%
l998
20.0%
M501
 
10.0%
F497
 
10.0%
m497
 
10.0%

primary_date
Date

Primary reference date

Distinct301
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
Minimum2017-01-23 00:00:00
Maximum2018-07-24 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:10:33.861784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.909850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BMI (kg/m²)
Real number (ℝ)

High correlation 

Body Mass Index

Distinct816
Distinct (%)82.2%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean29.560846
Minimum15.24
Maximum65.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:33.954749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.24
5-th percentile18.286
Q123.53
median29.07
Q334.52
95-th percentile42.696
Maximum65.89
Range50.65
Interquartile range (IQR)10.99

Descriptive statistics

Standard deviation7.7932914
Coefficient of variation (CV)0.2636356
Kurtosis0.67413325
Mean29.560846
Median Absolute Deviation (MAD)5.49
Skewness0.63362711
Sum29353.92
Variance60.735391
MonotonicityNot monotonic
2025-11-25T07:10:33.999273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.473
 
0.3%
34.763
 
0.3%
31.263
 
0.3%
32.573
 
0.3%
30.943
 
0.3%
26.963
 
0.3%
28.553
 
0.3%
27.853
 
0.3%
30.53
 
0.3%
37.23
 
0.3%
Other values (806)963
96.5%
(Missing)5
 
0.5%
ValueCountFrequency (%)
15.241
0.1%
15.31
0.1%
15.391
0.1%
15.461
0.1%
15.691
0.1%
15.851
0.1%
15.931
0.1%
16.141
0.1%
16.211
0.1%
16.341
0.1%
ValueCountFrequency (%)
65.891
0.1%
62.841
0.1%
58.621
0.1%
56.421
0.1%
56.091
0.1%
53.911
0.1%
53.851
0.1%
53.131
0.1%
52.891
0.1%
51.371
0.1%

weight_kg
Real number (ℝ)

High correlation 

Body weight in kilograms

Distinct529
Distinct (%)53.3%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean79.47718
Minimum37
Maximum168.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.045909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile51.9
Q165.2
median78.1
Q390.9
95-th percentile113
Maximum168.8
Range131.8
Interquartile range (IQR)25.7

Descriptive statistics

Standard deviation19.199436
Coefficient of variation (CV)0.24157168
Kurtosis0.76277468
Mean79.47718
Median Absolute Deviation (MAD)12.9
Skewness0.63115531
Sum78920.84
Variance368.61833
MonotonicityNot monotonic
2025-11-25T07:10:34.179456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.87
 
0.7%
77.26
 
0.6%
74.26
 
0.6%
66.66
 
0.6%
63.95
 
0.5%
85.35
 
0.5%
90.65
 
0.5%
80.75
 
0.5%
745
 
0.5%
60.55
 
0.5%
Other values (519)938
94.0%
ValueCountFrequency (%)
371
0.1%
38.31
0.1%
39.71
0.1%
40.71
0.1%
40.91
0.1%
42.21
0.1%
42.41
0.1%
42.91
0.1%
43.51
0.1%
43.81
0.1%
ValueCountFrequency (%)
168.81
0.1%
162.21
0.1%
153.91
0.1%
144.51
0.1%
1431
0.1%
136.81
0.1%
134.81
0.1%
134.71
0.1%
133.61
0.1%
133.41
0.1%

height_m
Real number (ℝ)

High correlation 

Height in meters

Distinct50
Distinct (%)5.0%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.646858
Minimum1.39
Maximum1.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.227456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.39
5-th percentile1.5
Q11.58
median1.64
Q31.71
95-th percentile1.79
Maximum1.92
Range0.53
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.090536021
Coefficient of variation (CV)0.054975001
Kurtosis-0.40941396
Mean1.646858
Median Absolute Deviation (MAD)0.07
Skewness0.11788112
Sum1635.33
Variance0.0081967711
MonotonicityNot monotonic
2025-11-25T07:10:34.276643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5853
 
5.3%
1.6342
 
4.2%
1.7442
 
4.2%
1.6239
 
3.9%
1.738
 
3.8%
1.5938
 
3.8%
1.6938
 
3.8%
1.6438
 
3.8%
1.6737
 
3.7%
1.5737
 
3.7%
Other values (40)591
59.2%
ValueCountFrequency (%)
1.391
 
0.1%
1.443
 
0.3%
1.456
 
0.6%
1.469
0.9%
1.475
 
0.5%
1.489
0.9%
1.497
0.7%
1.511
1.1%
1.516
 
0.6%
1.5216
1.6%
ValueCountFrequency (%)
1.921
 
0.1%
1.912
 
0.2%
1.91
 
0.1%
1.891
 
0.1%
1.881
 
0.1%
1.873
 
0.3%
1.861
 
0.1%
1.853
 
0.3%
1.842
 
0.2%
1.8310
1.0%

systolic_bp_mmHg
Real number (ℝ)

High correlation 

Systolic blood pressure

Distinct196
Distinct (%)19.8%
Missing7
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean132.32341
Minimum81
Maximum258.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.323667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile103
Q1117.75
median130
Q3144
95-th percentile169.75
Maximum258.5
Range177.5
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation21.355436
Coefficient of variation (CV)0.16138819
Kurtosis2.2537289
Mean132.32341
Median Absolute Deviation (MAD)13
Skewness0.92951207
Sum131132.5
Variance456.05464
MonotonicityNot monotonic
2025-11-25T07:10:34.371357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12316
 
1.6%
124.515
 
1.5%
117.515
 
1.5%
136.515
 
1.5%
11915
 
1.5%
118.514
 
1.4%
126.514
 
1.4%
12414
 
1.4%
142.513
 
1.3%
134.513
 
1.3%
Other values (186)847
84.9%
ValueCountFrequency (%)
811
0.1%
851
0.1%
86.51
0.1%
871
0.1%
87.51
0.1%
881
0.1%
88.51
0.1%
891
0.1%
89.51
0.1%
902
0.2%
ValueCountFrequency (%)
258.51
0.1%
2391
0.1%
2151
0.1%
2131
0.1%
2111
0.1%
208.51
0.1%
207.51
0.1%
206.51
0.1%
193.51
0.1%
1921
0.1%

diastolic_bp_mmHg
Real number (ℝ)

High correlation 

Diastolic blood pressure

Distinct133
Distinct (%)13.4%
Missing7
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean88.397074
Minimum48.5
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.419172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48.5
5-th percentile70
Q180
median87.5
Q396
95-th percentile109.5
Maximum150
Range101.5
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.449453
Coefficient of variation (CV)0.14083558
Kurtosis1.1474371
Mean88.397074
Median Absolute Deviation (MAD)8
Skewness0.55858342
Sum87601.5
Variance154.98889
MonotonicityNot monotonic
2025-11-25T07:10:34.466063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.523
 
2.3%
8623
 
2.3%
87.522
 
2.2%
9622
 
2.2%
83.521
 
2.1%
8421
 
2.1%
8320
 
2.0%
90.520
 
2.0%
9020
 
2.0%
86.520
 
2.0%
Other values (123)779
78.1%
ValueCountFrequency (%)
48.51
0.1%
561
0.1%
571
0.1%
58.51
0.1%
601
0.1%
60.51
0.1%
612
0.2%
61.51
0.1%
62.51
0.1%
632
0.2%
ValueCountFrequency (%)
1501
0.1%
141.51
0.1%
133.51
0.1%
131.51
0.1%
1311
0.1%
1301
0.1%
129.51
0.1%
125.52
0.2%
1251
0.1%
122.52
0.2%

Albumin (g/dL)
Real number (ℝ)

Missing 

Serum albumin

Distinct42
Distinct (%)4.5%
Missing56
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean41.72293
Minimum29.5
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.509198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile37
Q140
median42
Q343.5
95-th percentile46
Maximum60
Range30.5
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.0735208
Coefficient of variation (CV)0.073665027
Kurtosis3.1810547
Mean41.72293
Median Absolute Deviation (MAD)2
Skewness0.32038988
Sum39303
Variance9.44653
MonotonicityNot monotonic
2025-11-25T07:10:34.554787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41118
11.8%
42109
10.9%
4395
 
9.5%
4072
 
7.2%
4460
 
6.0%
4550
 
5.0%
3950
 
5.0%
3849
 
4.9%
41.537
 
3.7%
40.533
 
3.3%
Other values (32)269
27.0%
(Missing)56
 
5.6%
ValueCountFrequency (%)
29.51
 
0.1%
301
 
0.1%
312
 
0.2%
31.51
 
0.1%
32.51
 
0.1%
331
 
0.1%
343
 
0.3%
357
 
0.7%
35.51
 
0.1%
3618
1.8%
ValueCountFrequency (%)
601
 
0.1%
581
 
0.1%
561
 
0.1%
541
 
0.1%
522
 
0.2%
502
 
0.2%
495
0.5%
48.53
0.3%
486
0.6%
47.53
0.3%

Total protein (g/dL)
Real number (ℝ)

Missing 

Total serum protein

Distinct868
Distinct (%)93.4%
Missing69
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean55.019494
Minimum12.09
Maximum261.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.600544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.09
5-th percentile24.886
Q137.15
median49.1
Q366.7
95-th percentile100.98
Maximum261.25
Range249.16
Interquartile range (IQR)29.55

Descriptive statistics

Standard deviation26.854133
Coefficient of variation (CV)0.48808398
Kurtosis7.6577044
Mean55.019494
Median Absolute Deviation (MAD)14.07
Skewness2.0273365
Sum51113.11
Variance721.14448
MonotonicityNot monotonic
2025-11-25T07:10:34.648760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.723
 
0.3%
32.773
 
0.3%
54.923
 
0.3%
65.152
 
0.2%
59.782
 
0.2%
30.582
 
0.2%
44.922
 
0.2%
42.072
 
0.2%
35.452
 
0.2%
62.492
 
0.2%
Other values (858)906
90.8%
(Missing)69
 
6.9%
ValueCountFrequency (%)
12.091
0.1%
12.991
0.1%
14.381
0.1%
16.251
0.1%
17.441
0.1%
17.521
0.1%
18.021
0.1%
18.141
0.1%
18.151
0.1%
18.411
0.1%
ValueCountFrequency (%)
261.251
0.1%
209.41
0.1%
194.441
0.1%
184.781
0.1%
184.581
0.1%
174.971
0.1%
167.661
0.1%
166.311
0.1%
162.31
0.1%
150.851
0.1%

total_cholesterol_mg_dL
Real number (ℝ)

Missing 

Total cholesterol

Distinct369
Distinct (%)38.0%
Missing26
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean4.3946914
Minimum1.6
Maximum11.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.697518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile2.8555
Q13.68
median4.35
Q35.02
95-th percentile6.1745
Maximum11.98
Range10.38
Interquartile range (IQR)1.34

Descriptive statistics

Standard deviation1.0298543
Coefficient of variation (CV)0.23434052
Kurtosis2.8989069
Mean4.3946914
Median Absolute Deviation (MAD)0.67
Skewness0.72446762
Sum4271.64
Variance1.0605998
MonotonicityNot monotonic
2025-11-25T07:10:34.744048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1111
 
1.1%
4.0410
 
1.0%
4.759
 
0.9%
3.758
 
0.8%
4.458
 
0.8%
5.427
 
0.7%
3.557
 
0.7%
4.377
 
0.7%
4.097
 
0.7%
4.087
 
0.7%
Other values (359)891
89.3%
(Missing)26
 
2.6%
ValueCountFrequency (%)
1.61
0.1%
1.81
0.1%
1.821
0.1%
21
0.1%
2.151
0.1%
2.181
0.1%
2.21
0.1%
2.281
0.1%
2.331
0.1%
2.351
0.1%
ValueCountFrequency (%)
11.981
0.1%
7.821
0.1%
7.711
0.1%
7.61
0.1%
7.431
0.1%
7.31
0.1%
7.241
0.1%
7.21
0.1%
7.091
0.1%
7.082
0.2%

Triglycerides (mg/dL)
Real number (ℝ)

Missing 

Triglycerides

Distinct207
Distinct (%)21.3%
Missing26
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1.0413786
Minimum0.22
Maximum10.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.791656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.42
Q10.64
median0.85
Q31.21
95-th percentile2.1545
Maximum10.42
Range10.2
Interquartile range (IQR)0.57

Descriptive statistics

Standard deviation0.74350213
Coefficient of variation (CV)0.71395949
Kurtosis38.239011
Mean1.0413786
Median Absolute Deviation (MAD)0.255
Skewness4.6665268
Sum1012.22
Variance0.55279542
MonotonicityNot monotonic
2025-11-25T07:10:34.837846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8217
 
1.7%
0.8117
 
1.7%
0.7116
 
1.6%
0.6616
 
1.6%
0.6915
 
1.5%
0.8314
 
1.4%
0.5414
 
1.4%
0.6713
 
1.3%
0.6513
 
1.3%
0.7813
 
1.3%
Other values (197)824
82.6%
(Missing)26
 
2.6%
ValueCountFrequency (%)
0.221
 
0.1%
0.251
 
0.1%
0.281
 
0.1%
0.32
 
0.2%
0.322
 
0.2%
0.334
0.4%
0.344
0.4%
0.356
0.6%
0.362
 
0.2%
0.375
0.5%
ValueCountFrequency (%)
10.421
0.1%
7.241
0.1%
5.62
0.2%
5.451
0.1%
5.241
0.1%
5.131
0.1%
5.011
0.1%
4.541
0.1%
4.41
0.1%
4.061
0.1%

fasting_glucose_mmol_L
Real number (ℝ)

Missing 

Fasting blood glucose (mmol/L)

Distinct360
Distinct (%)37.0%
Missing24
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5.3995483
Minimum3.13
Maximum29.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:34.884595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.13
5-th percentile3.88
Q14.5125
median5.03
Q35.59
95-th percentile8.1415
Maximum29.76
Range26.63
Interquartile range (IQR)1.0775

Descriptive statistics

Standard deviation1.9540223
Coefficient of variation (CV)0.36188625
Kurtosis40.469736
Mean5.3995483
Median Absolute Deviation (MAD)0.54
Skewness5.1699169
Sum5259.16
Variance3.8182031
MonotonicityNot monotonic
2025-11-25T07:10:34.932088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0310
 
1.0%
4.759
 
0.9%
4.999
 
0.9%
4.968
 
0.8%
5.168
 
0.8%
5.198
 
0.8%
4.878
 
0.8%
4.278
 
0.8%
5.057
 
0.7%
5.027
 
0.7%
Other values (350)892
89.4%
(Missing)24
 
2.4%
ValueCountFrequency (%)
3.131
0.1%
3.211
0.1%
3.371
0.1%
3.421
0.1%
3.441
0.1%
3.451
0.1%
3.471
0.1%
3.481
0.1%
3.51
0.1%
3.552
0.2%
ValueCountFrequency (%)
29.761
0.1%
20.211
0.1%
20.031
0.1%
19.781
0.1%
17.71
0.1%
16.791
0.1%
16.441
0.1%
14.441
0.1%
14.371
0.1%
14.191
0.1%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
0.0
998 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2994
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0998
100.0%

Length

2025-11-25T07:10:34.978330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:35.009238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0998
100.0%

Most occurring characters

ValueCountFrequency (%)
01996
66.7%
.998
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1996
66.7%
Other Punctuation998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01996
100.0%
Other Punctuation
ValueCountFrequency (%)
.998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01996
66.7%
.998
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01996
66.7%
.998
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
1.0
998 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0998
100.0%

Length

2025-11-25T07:10:35.043378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:35.074893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0998
100.0%

Most occurring characters

ValueCountFrequency (%)
1998
33.3%
.998
33.3%
0998
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1996
66.7%
Other Punctuation998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1998
50.0%
0998
50.0%
Other Punctuation
ValueCountFrequency (%)
.998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1998
33.3%
.998
33.3%
0998
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1998
33.3%
.998
33.3%
0998
33.3%

waist_circ_unit_correction_applied
Boolean

Constant 

Quality flag: Waist circumference unit corrected

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
False
998 
ValueCountFrequency (%)
False998
100.0%
2025-11-25T07:10:35.099981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

sa_biomarker_standards
Categorical

Constant 

South African biomarker reference standards applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
1.0
998 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0998
100.0%

Length

2025-11-25T07:10:35.134958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:35.167266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0998
100.0%

Most occurring characters

ValueCountFrequency (%)
1998
33.3%
.998
33.3%
0998
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1996
66.7%
Other Punctuation998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1998
50.0%
0998
50.0%
Other Punctuation
ValueCountFrequency (%)
.998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1998
33.3%
.998
33.3%
0998
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1998
33.3%
.998
33.3%
0998
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Daily mean temperature

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.981225
Minimum5.819
Maximum20.448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:35.194316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.819
5-th percentile11.215
Q113.167
median14.301
Q317.789
95-th percentile20.056
Maximum20.448
Range14.629
Interquartile range (IQR)4.622

Descriptive statistics

Standard deviation3.1889121
Coefficient of variation (CV)0.21286057
Kurtosis-0.15874256
Mean14.981225
Median Absolute Deviation (MAD)2.203
Skewness-0.054256073
Sum14951.263
Variance10.16916
MonotonicityNot monotonic
2025-11-25T07:10:35.233163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
15.29893
 
9.3%
13.43990
 
9.0%
16.50476
 
7.6%
11.92476
 
7.6%
18.56775
 
7.5%
19.63366
 
6.6%
11.21564
 
6.4%
13.24761
 
6.1%
13.73459
 
5.9%
11.28556
 
5.6%
Other values (9)282
28.3%
ValueCountFrequency (%)
5.81921
 
2.1%
11.21564
6.4%
11.28556
5.6%
11.92476
7.6%
13.16749
4.9%
13.24761
6.1%
13.43990
9.0%
13.73459
5.9%
14.05610
 
1.0%
14.30117
 
1.7%
ValueCountFrequency (%)
20.44844
4.4%
20.05635
 
3.5%
19.7396
 
0.6%
19.63366
6.6%
18.56775
7.5%
17.78950
5.0%
16.50476
7.6%
15.29893
9.3%
14.8350
5.0%
14.30117
 
1.7%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Daily maximum temperature

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.61311
Minimum14.704
Maximum26.904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:35.272521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14.704
5-th percentile17.385
Q119.286
median21.664
Q325.097
95-th percentile25.351
Maximum26.904
Range12.2
Interquartile range (IQR)5.811

Descriptive statistics

Standard deviation2.9104407
Coefficient of variation (CV)0.13466089
Kurtosis-0.94979684
Mean21.61311
Median Absolute Deviation (MAD)2.852
Skewness-0.16838095
Sum21569.884
Variance8.4706652
MonotonicityNot monotonic
2025-11-25T07:10:35.312834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
22.91393
 
9.3%
19.28690
 
9.0%
25.35176
 
7.6%
18.69976
 
7.6%
23.03275
 
7.5%
25.09766
 
6.6%
17.38564
 
6.4%
20.8161
 
6.1%
21.66459
 
5.9%
18.81256
 
5.6%
Other values (9)282
28.3%
ValueCountFrequency (%)
14.70421
 
2.1%
15.72210
 
1.0%
17.38564
6.4%
18.69976
7.6%
18.81256
5.6%
19.28690
9.0%
19.47750
5.0%
20.8161
6.1%
20.81949
4.9%
21.66459
5.9%
ValueCountFrequency (%)
26.9046
 
0.6%
25.49335
 
3.5%
25.35176
7.6%
25.31244
4.4%
25.10450
5.0%
25.09766
6.6%
23.03275
7.5%
22.91393
9.3%
22.07917
 
1.7%
21.66459
5.9%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Daily minimum temperature

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6845381
Minimum-0.933
Maximum16.914
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)2.1%
Memory size15.6 KiB
2025-11-25T07:10:35.348947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.933
5-th percentile4.536
Q15.53
median6.626
Q311.883
95-th percentile15.349
Maximum16.914
Range17.847
Interquartile range (IQR)6.353

Descriptive statistics

Standard deviation4.163937
Coefficient of variation (CV)0.47946557
Kurtosis-0.65407664
Mean8.6845381
Median Absolute Deviation (MAD)1.728
Skewness0.46207105
Sum8667.169
Variance17.338371
MonotonicityNot monotonic
2025-11-25T07:10:35.386716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8.35193
 
9.3%
6.62290
 
9.0%
6.12176
 
7.6%
4.89876
 
7.6%
15.34975
 
7.5%
14.92266
 
6.6%
6.62664
 
6.4%
4.72161
 
6.1%
5.5359
 
5.9%
4.53656
 
5.6%
Other values (9)282
28.3%
ValueCountFrequency (%)
-0.93321
 
2.1%
4.53656
5.6%
4.72161
6.1%
4.89876
7.6%
5.5359
5.9%
6.12176
7.6%
6.62290
9.0%
6.62664
6.4%
7.1617
 
1.7%
8.12149
4.9%
ValueCountFrequency (%)
16.91435
 
3.5%
15.34975
7.5%
14.92266
6.6%
14.38944
4.4%
12.39110
 
1.0%
11.88350
5.0%
10.48850
5.0%
10.1876
 
0.6%
8.35193
9.3%
8.12149
4.9%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Temperature anomaly from baseline

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.085012
Minimum-3.11
Maximum9.491
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)1.0%
Memory size15.6 KiB
2025-11-25T07:10:35.426495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.11
5-th percentile1.389
Q14.839
median6.775
Q37.413
95-th percentile9.491
Maximum9.491
Range12.601
Interquartile range (IQR)2.574

Descriptive statistics

Standard deviation2.2374406
Coefficient of variation (CV)0.36769699
Kurtosis1.8581079
Mean6.085012
Median Absolute Deviation (MAD)1.071
Skewness-1.1131121
Sum6072.842
Variance5.0061405
MonotonicityNot monotonic
2025-11-25T07:10:35.465936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5.89993
 
9.3%
7.09390
 
9.0%
7.84676
 
7.6%
8.64576
 
7.6%
3.0875
 
7.5%
6.77566
 
6.6%
5.91764
 
6.4%
1.38961
 
6.1%
9.49159
 
5.9%
7.25156
 
5.6%
Other values (9)282
28.3%
ValueCountFrequency (%)
-3.1110
 
1.0%
1.38961
6.1%
3.0875
7.5%
4.00550
5.0%
4.42935
 
3.5%
4.83921
 
2.1%
5.37744
4.4%
5.89993
9.3%
5.91764
6.4%
6.54417
 
1.7%
ValueCountFrequency (%)
9.49159
5.9%
8.64576
7.6%
7.84676
7.6%
7.76
 
0.6%
7.41349
4.9%
7.25156
5.6%
7.09390
9.0%
6.83850
5.0%
6.77566
6.6%
6.54417
 
1.7%

climate_heat_day_p90
Categorical

Constant 

Heat day indicator (>90th percentile)

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
0.0
998 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2994
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0998
100.0%

Length

2025-11-25T07:10:35.507914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:35.539098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0998
100.0%

Most occurring characters

ValueCountFrequency (%)
01996
66.7%
.998
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1996
66.7%
Other Punctuation998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01996
100.0%
Other Punctuation
ValueCountFrequency (%)
.998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01996
66.7%
.998
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01996
66.7%
.998
33.3%

climate_heat_day_p95
Categorical

Constant 

Heat day indicator (>95th percentile)

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
0.0
998 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2994
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0998
100.0%

Length

2025-11-25T07:10:35.572349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:35.603398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0998
100.0%

Most occurring characters

ValueCountFrequency (%)
01996
66.7%
.998
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1996
66.7%
Other Punctuation998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01996
100.0%
Other Punctuation
ValueCountFrequency (%)
.998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01996
66.7%
.998
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01996
66.7%
.998
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Heat stress index

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.441621
Minimum6.735
Maximum23.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T07:10:35.631291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.735
5-th percentile13.476
Q113.864
median15.87
Q319.354
95-th percentile21.21735
Maximum23.6
Range16.865
Interquartile range (IQR)5.49

Descriptive statistics

Standard deviation3.1486077
Coefficient of variation (CV)0.19150226
Kurtosis0.36253918
Mean16.441621
Median Absolute Deviation (MAD)2.088
Skewness-0.16105916
Sum16408.738
Variance9.9137301
MonotonicityNot monotonic
2025-11-25T07:10:35.667352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
13.78293
 
9.3%
15.8790
 
9.0%
19.35476
 
7.6%
14.0576
 
7.6%
19.46875
 
7.5%
21.0366
 
6.6%
13.47664
 
6.4%
16.67661
 
6.1%
17.49159
 
5.9%
13.86456
 
5.6%
Other values (9)282
28.3%
ValueCountFrequency (%)
6.73521
 
2.1%
10.74210
 
1.0%
13.47664
6.4%
13.4950
5.0%
13.78293
9.3%
13.86456
5.6%
14.0576
7.6%
15.40849
4.9%
15.8790
9.0%
16.59850
5.0%
ValueCountFrequency (%)
23.66
 
0.6%
22.27944
4.4%
21.0366
6.6%
19.90735
3.5%
19.46875
7.5%
19.35476
7.6%
17.49159
5.9%
17.47117
 
1.7%
16.67661
6.1%
16.59850
5.0%

climate_season
Categorical

High correlation 

Season

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size61.4 KiB
Winter
307 
Autumn
291 
Spring
230 
Summer
170 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5988
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowSummer
3rd rowSummer
4th rowSummer
5th rowSummer

Common Values

ValueCountFrequency (%)
Winter307
30.8%
Autumn291
29.2%
Spring230
23.0%
Summer170
17.0%

Length

2025-11-25T07:10:35.711427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:35.746596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
winter307
30.8%
autumn291
29.2%
spring230
23.0%
summer170
17.0%

Most occurring characters

ValueCountFrequency (%)
n828
13.8%
u752
12.6%
r707
11.8%
m631
10.5%
t598
10.0%
i537
9.0%
e477
8.0%
S400
6.7%
W307
 
5.1%
A291
 
4.9%
Other values (2)460
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4990
83.3%
Uppercase Letter998
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n828
16.6%
u752
15.1%
r707
14.2%
m631
12.6%
t598
12.0%
i537
10.8%
e477
9.6%
p230
 
4.6%
g230
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S400
40.1%
W307
30.8%
A291
29.2%

Most occurring scripts

ValueCountFrequency (%)
Latin5988
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n828
13.8%
u752
12.6%
r707
11.8%
m631
10.5%
t598
10.0%
i537
9.0%
e477
8.0%
S400
6.7%
W307
 
5.1%
A291
 
4.9%
Other values (2)460
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n828
13.8%
u752
12.6%
r707
11.8%
m631
10.5%
t598
10.0%
i537
9.0%
e477
8.0%
S400
6.7%
W307
 
5.1%
A291
 
4.9%
Other values (2)460
7.7%

Interactions

2025-11-25T07:10:32.739355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.067018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.676965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.221302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.893942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.437678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.014559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.566593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.180560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.711156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.245363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.777528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.391745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.950753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.514699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.095843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.774972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.101474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.710865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.337648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.927993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.475298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.048317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.600935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.214104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.744152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.278298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.811104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.427545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.984340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.551886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.131862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.808070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.140414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.741479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.372941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.961541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.510814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.081628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.632765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.246061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.775567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.310457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.841788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.461846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.016183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.587129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.165247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.844848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.179987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.773481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.417534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.993920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.548319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.115684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.746205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.279274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.808713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.343184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.875933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.497823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.048785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.623096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.201883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.878875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.232647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.808480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.470862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.026860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.585344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.148721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.779674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.311515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.842540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.374789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.907511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.531687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.081878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.659089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.318040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.918752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.280713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.843722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.510003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.061547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.621623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.185014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.815933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.346110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.876609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.411528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.022847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.568904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.119311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.695766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.353421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.953506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.315838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.874162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.543411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.091625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.656329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.217335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.846663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.377195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.908242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.442184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.054079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.601419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.153758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.731007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.388207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.992114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.355511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.906772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.580025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.125278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.694010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.254798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.878914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.410335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.942429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.476602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.088434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.635810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.188758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.767918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.423348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.027818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.389063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.938500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.613126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.156401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.728079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.289029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.910687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.440923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.974660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.508077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.120334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.669125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.225490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.804292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.457577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.063973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.422969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.972850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.647289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.189251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.763655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.322042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.944302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.473795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.007923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.539797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.153163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.703690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.262024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.838777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.490532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.101089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.456591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.006791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.681761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.221772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.797196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.354393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.977092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.506495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.040084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.571242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.185868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.738182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.299321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.874257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.525545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.140914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.491254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.039147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.714616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.255847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.831682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.387700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.010145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.540090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.073504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.603928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.218049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.772777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.336154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.911137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.559362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.180097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.526949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.077652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.750363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.291783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.868906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.423506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.044614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.574427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.108960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.638869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.253940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.809628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.373090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.948237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.596558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.215832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.563565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.115400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.782733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.325291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.904475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.456122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.078095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.607791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.141260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.671937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.286514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.843327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.408464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.985549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.630486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.256418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.602190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.152011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.818100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.364046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.941526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.493971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.112450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.643190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.177247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.709183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.323572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.879981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.442736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.022077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.667485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:33.294484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:24.638331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.185616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:25.855572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.399683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:26.977401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:27.528423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.146423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:28.676310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.209709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:29.742921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.356767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:30.915777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:31.477272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.058687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:32.701830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:10:35.861831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)Albumin (g/dL)BMI (kg/m²)SexTotal protein (g/dL)Triglycerides (mg/dL)climate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_stress_indexclimate_seasonclimate_temp_anomalydiastolic_bp_mmHgfasting_glucose_mmol_Lheight_msystolic_bp_mmHgtotal_cholesterol_mg_dLweight_kg
Age (at enrolment)1.000-0.0440.1200.138-0.1390.0450.1010.1440.1360.0360.083-0.1030.0100.105-0.0520.2000.0910.118
Albumin (g/dL)-0.0441.000-0.1850.2630.0950.193-0.051-0.101-0.108-0.0380.0000.1320.1360.0670.1980.1180.191-0.106
BMI (kg/m²)0.120-0.1851.0000.507-0.1340.1340.0510.0830.0940.0300.073-0.1750.1850.241-0.4240.1880.1110.901
Sex0.1380.2630.5071.0000.2680.1650.1640.3400.2790.3820.2560.4410.0190.0360.7590.0530.1520.248
Total protein (g/dL)-0.1390.095-0.1340.2681.0000.104-0.090-0.103-0.096-0.0490.0720.1050.062-0.0150.2600.008-0.006-0.031
Triglycerides (mg/dL)0.0450.1930.1340.1650.1041.000-0.037-0.032-0.0360.0100.0580.0580.1510.2980.1210.1630.2700.208
climate_daily_max_temp0.101-0.0510.0510.164-0.090-0.0371.0000.9050.5820.7640.587-0.098-0.096-0.103-0.066-0.0690.0330.024
climate_daily_mean_temp0.144-0.1010.0830.340-0.103-0.0320.9051.0000.7800.7450.682-0.288-0.121-0.046-0.111-0.0800.0250.040
climate_daily_min_temp0.136-0.1080.0940.279-0.096-0.0360.5820.7801.0000.3950.704-0.474-0.128-0.037-0.140-0.071-0.0080.044
climate_heat_stress_index0.036-0.0380.0300.382-0.0490.0100.7640.7450.3951.0000.657-0.002-0.0530.077-0.019-0.0490.0120.019
climate_season0.0830.0000.0730.2560.0720.0580.5870.6820.7040.6571.0000.6100.0620.0900.1000.0500.0650.000
climate_temp_anomaly-0.1030.132-0.1750.4410.1050.058-0.098-0.288-0.474-0.0020.6101.0000.1310.1350.2230.1070.042-0.095
diastolic_bp_mmHg0.0100.1360.1850.0190.0620.151-0.096-0.121-0.128-0.0530.0620.1311.0000.167-0.0010.7970.0830.198
fasting_glucose_mmol_L0.1050.0670.2410.036-0.0150.298-0.103-0.046-0.0370.0770.0900.1350.1671.000-0.0190.1740.0650.260
height_m-0.0520.198-0.4240.7590.2600.121-0.066-0.111-0.140-0.0190.1000.223-0.001-0.0191.000-0.002-0.108-0.021
systolic_bp_mmHg0.2000.1180.1880.0530.0080.163-0.069-0.080-0.071-0.0490.0500.1070.7970.174-0.0021.0000.0840.199
total_cholesterol_mg_dL0.0910.1910.1110.152-0.0060.2700.0330.025-0.0080.0120.0650.0420.0830.065-0.1080.0841.0000.075
weight_kg0.118-0.1060.9010.248-0.0310.2080.0240.0400.0440.0190.000-0.0950.1980.260-0.0210.1990.0751.000

Missing values

2025-11-25T07:10:33.354586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:10:33.474512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:10:33.559698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceAge (at enrolment)Sexprimary_dateBMI (kg/m²)weight_kgheight_msystolic_bp_mmHgdiastolic_bp_mmHgAlbumin (g/dL)Total protein (g/dL)total_cholesterol_mg_dLTriglycerides (mg/dL)fasting_glucose_mmol_Lcd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
985JHB_DPHRU_05362.0Female2017-01-2626.7874.81.67148.589.043.065.815.581.647.590.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
986JHB_DPHRU_05354.0Female2017-02-1129.5288.81.73149.5100.543.567.052.911.464.440.01.0False1.020.44825.31214.3895.3770.00.022.279Summer
987JHB_DPHRU_05362.0Female2017-01-2317.7759.41.83154.093.043.066.704.040.504.320.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
988JHB_DPHRU_05354.0Female2017-01-2720.4548.61.54124.572.044.0100.924.280.804.220.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
989JHB_DPHRU_05358.0Female2017-01-3133.9998.21.70117.572.540.075.005.220.906.760.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
990JHB_DPHRU_05358.0Female2017-01-3123.4672.81.76134.084.041.0NaN4.620.734.300.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
991JHB_DPHRU_05360.0Female2017-02-1521.4058.41.65139.086.035.052.662.875.244.640.01.0False1.020.44825.31214.3895.3770.00.022.279Summer
992JHB_DPHRU_05359.0Female2017-02-0320.7862.21.73104.075.041.543.693.071.313.470.01.0False1.020.44825.31214.3895.3770.00.022.279Summer
993JHB_DPHRU_05353.0Female2017-01-2521.3056.21.62107.077.043.5100.033.860.814.100.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
994JHB_DPHRU_05359.0Female2017-03-1526.0080.61.76139.588.554.057.295.561.304.610.01.0False1.017.78925.10411.8836.8380.00.016.598Autumn
study_sourceAge (at enrolment)Sexprimary_dateBMI (kg/m²)weight_kgheight_msystolic_bp_mmHgdiastolic_bp_mmHgAlbumin (g/dL)Total protein (g/dL)total_cholesterol_mg_dLTriglycerides (mg/dL)fasting_glucose_mmol_Lcd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
1973JHB_DPHRU_05354.0Male2018-03-2035.8492.91.61119.094.542.034.925.830.915.050.01.0False1.019.63325.09714.9226.7750.00.021.030Autumn
1974JHB_DPHRU_05360.0Male2018-04-2427.8674.61.64130.088.541.570.454.610.735.650.01.0False1.014.83019.47710.4884.0050.00.013.490Autumn
1975JHB_DPHRU_05353.0Male2018-03-0135.0090.51.61NaNNaNNaN75.51NaNNaNNaN0.01.0False1.019.63325.09714.9226.7750.00.021.030Autumn
1976JHB_DPHRU_05353.0Male2018-02-2326.7963.21.54136.5103.541.030.484.370.784.680.01.0False1.018.56723.03215.3493.0800.00.019.468Summer
1977JHB_DPHRU_05354.0Male2018-02-2739.20106.61.65117.582.541.027.373.440.816.480.01.0False1.018.56723.03215.3493.0800.00.019.468Summer
1978JHB_DPHRU_05346.0Male2017-07-0526.3162.91.55154.0111.541.036.344.240.473.710.01.0False1.011.21517.3856.6265.9170.00.013.476Winter
1979JHB_DPHRU_05357.0Male2018-03-0736.7996.01.62146.596.043.036.194.411.555.110.01.0False1.019.63325.09714.9226.7750.00.021.030Autumn
1980JHB_DPHRU_05350.0Male2018-02-1537.20100.91.65144.589.5NaN32.773.770.804.860.01.0False1.018.56723.03215.3493.0800.00.019.468Summer
1981JHB_DPHRU_05343.0Male2017-11-2334.9986.11.57131.586.538.556.354.560.504.260.01.0False1.013.24720.8104.7211.3890.00.016.676Spring
1982JHB_DPHRU_05356.0Male2017-05-3033.8784.71.58160.599.541.035.866.751.125.680.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn